Natural language (NL) processing has achieved considerable progress in areas such as speech recognition end generation. Natural language systems have become commonplace, especially in server-based Internet applications, speech recognition products, database search tools, and other environments where human interaction is required. However, decades of hard work by some of the brightest minds in the Artificial Intelligence field has proven the understanding of speech one of the most evasive information technology goals. Researchers, therefore, have lowered their expectations for practical NL systems. For instance, existing NL prototypes focus on specific topics of conversation as more broader applications are more difficult to apply conventional NL techniques. Examples of these focused prototypes include the JUPITER telephone-based conversational system developed at the Massachusetts Institute of Technology that provides weather forecasts and the MOVIELINE system developed at Carnegie Mellon University that provides local movie schedules. These prototypes serve as a first step towards a broader range understanding of NL solutions. Alternatively, some leading industrial efforts concentrate on building a logical structure (conceptual networks—not linguistics) for a general dictionary to support understanding and translation (e.g., the MINDNET language processing software developed by Microsoft Corporation of Redmond, Wash.).
As applied in the database areas, existing natural language interfaces (NLIs) place severe restrictions on the syntax with which users articulate their natural queries. Users typically balk at such restrictions. Thus, prompted in part by the Internet, several systems (e.g., the ASK.COM® search engine of Ask Jeeves, Inc., Emeryville, Calif.) strive to make the restrictions invisible to users; but these restrictions are still within these systems. In these systems, accuracy of interpretation and effort required of the user depends on how closely a database query matches underlying “templates.” Same emerging designs for database queries use semantic models and dictionaries, in a spirit similar to the logical structure approach. However, NL systems have not achieved the accuracy and reliability expected of them.
Demand for natural queries continues to grow not only in the area of database searching but also in the area of Internet search engines. The ASK.COM® search engine is an example of an Internet search engine that allows a user to perform natural queries in the form of a free format input. However, the search engine's results rely on (1) its recognized keywords, (2) predefined keyword related to recognized keywords (text classification), and (3) predefined possible questions (templates) associated with each group of keywords from (1) and (2). Its processing steps are (1) capture all recognized keywords from the inputs, (2) determine all keywords tat relate to recognized keywords, (3) retrieve and display predefined questions (each question wit one keyword group). Though the ASK.COM® search engine looks natural, it functions structurally as a text classification system, and does not actually interpret natural language queries. Therefore, it does not have the capability of processing natural language queries.